The demand for electrical energy is not constant, as there are certain hours of each day when demand peaks at levels considerably higher than the remainder of the day. If utility companies buy energy during the peak demand periods, they have to pay a premium for transferring energy when the transmission lines are congested. Flat-rate electric tariffs shield most customers from fluctuations in energy costs, especially those caused by buying energy supplies on short notice. Utilities, however, are not insulated from these fluctuations.
When the market rate for electricity rises above the approved retail rate, utilities are caught in the middle, which can be financially disastrous. Utilities cannot simply pass price increases along to customers without regulatory approval. As such, utility companies, to protect themselves from widely fluctuating costs and to reduce peak demands, have started introducing various time-based pricing mechanisms. Existing mechanisms include time of use (TOU), critical peak pricing (CPP), real-time pricing (RTP) and peak load reduction credits (PLRC). None of the existing approaches, however, support a dynamic pricing scheme for end customers or support variable pricing curves based on customer profile.
By way of example, in TOU pricing systems supported by smart meters, there can be both a significant delay before information reaches consumers and significant gaps in energy data details. These delays and gaps can undercut the premise of how smart meter technologies will empower consumers to make decisions about their energy use based on real-time costs. Also, the current RTP schemes require the meters (at customer premises) to connect to the utility systems to obtain the current price. Such a centralized approach is inefficient, as it requires huge communication and computation resources.